Project Info

Logfound

Devpost

Inspiration

I'm a solo founder, and I noticed I kept forgetting why I made certain decisions while building projects. I had notes everywhere, but nothing that connected everything together. I wanted one place that could remember my journey and help me improve with every project. That's why I built LogFound—an AI workspace that helps founders and developers keep track of their work, decisions, and progress in one place.

What it does

LogFound is an AI-powered workspace for solo founders and small teams. It lets me log project updates, engineering decisions, release notes, and milestones in one timeline instead of spreading them across different apps. It also includes three AI agents: Founder Coach – helps me plan, prioritize, and stay focused. CTO Agent – helps with coding, architecture, debugging, and technical decisions. Learning Agent – looks at everything I've done and helps me learn from my progress. The goal isn't just to answer questions. The AI understands my project and helps me build faster with context.

How we built it

I built LogFound using Next.js, React, Tailwind CSS, Supabase, Groq, and Vercel. I used Codex throughout development to generate code, fix bugs, refactor components, and speed up development. I used GPT-5.6 to plan features, improve the user experience, design the workflow, write documentation, and solve development problems. Everything is designed around one workspace where AI can understand previous decisions instead of starting from scratch every time. Challenges This project pushed me hard. I spent hours debugging API issues while switching between Gemini, OpenAI, and finally Groq. GitHub integration became one of my biggest challenges because of Supabase authentication, UUID errors, workspace synchronization, and deployment issues. I also had to fix environment variables, API keys, database configuration, and deployment problems while racing against the hackathon deadline. Even though some GitHub features are still being completed, I kept improving the project instead of giving up. What I learned I learned that building AI products isn't only about connecting an API. It's about creating a great experience, understanding user context, and solving real problems. I also learned how important debugging, persistence, and planning are. Every bug I fixed taught me something new.

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • Next.jsIn code
  • ReactIn code
  • SQLIn code
  • SupabaseIn code
  • Tailwind CSSIn code
  • TypeScriptIn code
  • JavaScriptClaimed
  • VercelClaimed

7 of 9 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

323 KB

Source files

89

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

0 stars